A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problemsolving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system.Comment: Proceedings of the 7th Mexican International Conference on Artificial Intelligence (MICAI 2008), Springer, pp. 765-77

An Interaction Centred Approach to the teaching of Non-technical Skills in a Virtual Environment

In most domains involving expert knowledge, there is a number of cognitive and social factors influencing how efficient one human being is at correctly assessing and responding to certain situations. These factors, which contribute to the efficient and safe realization of a technical activity, are known as non-technical skills, and correspond to a wide range of cognitive proficiencies such as situation awareness, decision making, stress or fatigue management, but also social skills such as communication, leadership and team working. Different studies have shown the impact such skills can have in the successful resolving of a number of critical situations, even more so in our domains of interest which are medical surgery or driving. In this paper, we take a look at the difficulties raised by the teaching of the technical and non-technical skills mobilized during a critical situation, in the context of TEL within virtual environments. We present the advantages of using a combined enactive and situated learning approach to this problematic, and then take an ill-defined perspective to raise some important designing issues in this respect. We show that some aspects of this problem have not been encompassed yet in the ill-defined domains literature, and should be further studied in any attempt at teaching behaviours inducing technical and non-technical skills in a virtual world

Contextual Sequential Pattern Mining

International audienceTraditional sequential patterns do not take into account additional contextual information since patterns extracted from data are usually general. By considering the fact that a pattern is associated with one specific context the decision expert can then adapt his strategy considering the type of customers. In this paper we propose to mine more precise patterns of the form "young users buy products A and B then product C, while old users do not follow this same behavior". By highlighting relevant properties of such contexts, we show how contextual sequential patterns can be extracted by mining the database in a concise manner. We conduct our experimental evaluation on real-world data and demonstrate performance issues

ADAPTIVE TUTORING E-LEARNING SYSTEM SUPPORTED BY DATA MINING

Inteligentni tutorski sustavi su računalni sustavi za učenje, razvijeni s ciljem da omoguće tutorski model učenja, koji svoju visoku efikasnost temelji na potpunoj prilagodbi procesa učenja jednom učeniku. Inteligencija, odnosno prilagodljivost sustava promjenjivim potrebama i razini znanja učenika ostvaruje se različitim pristupima, primjerice implementacijom algoritama strojnog učenja ili dubinskom analizom podataka. Inteligentni tutorski sustavi većinom se razvijaju za dobro definirane domene znanja, kao što su matematika, fizika i druge. Međutim, postoje brojna područja koja nemaju dobro definirane strukture znanja, a potrebno ih je poučavati. Takva područja nazivaju se slabo definiranim domenama znanja. Pripremu procesa učenja u slabo definiranim domenama obavlja učitelj koji ga oblikuje prema vlastitom znanju i iskustvu, a realizira se putem implementirane inteligencije tutorskog sustava. Implementacijom metoda za dubinsku analizu podataka možemo u podacima o interakcijama korisnika sa sustavom otkriti korisne informacije, te ih pridodati postojećim mogućnostima sustava kako bi poboljšali njegovu učinkovitost. U ovom radu predložen je novi model postojećeg inteligentnog tutorskog sustava koji, primjenom metoda dubinske analize podataka, sugerira korisniku korake koji mu slijede u procesu učenja, s ciljem kreiranja učinkovitijeg puta kroz domenu znanja. Ključni dijelovi unaprijeđenog tutorskog sustava su podsustavi za komunikaciju s alatima za dubinsku analizu podataka, grupiranje korisnika i otkrivanje čestih i učinkovitih puteva kroz domenu znanja. Predloženi tutorski model sustava prikazuje sugestije korisniku na početku i na kraju procesa učenja u obliku poveznica prema pojmovima u domeni znanja koje je najbolje učiti prije, odnosno nakon odabranog pojma. Opisani model sustava implementiran je web aplikacijom nazvanom DITUS (Department of Informatics TUtoring System). Djelotvornost predloženog modela vrednovana je analizom rezultata kontrolne i eksperimentalne skupine.Inteligent tutoring systems are e-learning systems, developed with the goal of emulating the tutoring teaching model, a highly efficient learning environment based on complete adaptation of the learning process to the needs of one student. The goal of such systems is that the computer system behaves in an intelligent way, e.g. that it adapts to the current needs and knowledge level of the student. This is achieved in various ways such as the application of artificial intelligence or data mining methods. Intelligent tutoring system are developed mostly for teaching in well defined knowledge domains such as mathematics, physics, etc., but there are many other not so well defined areas that also need to be taught. These areas are called ill-defined domains, where the teacher (expert) prepares the learning process based on his/hers knowledge and experience as well as the current „intelligence“ implemented in the system. By applying data mining methods to data about students' interactions with the system we can discover useful information about their learning processes and combine this information with the current system functionality in order to improve it's efficiency. This thesis proposes a new model of an existing intelligent tutoring system which, through the implementation of aforementioned methods, offers the student suggestions on which steps to learn next in order to streamline his/hers path through the knowledge domain. The key components of the presented system are: a communication layer for communication with data mining tools, a clustering model discovery and selection module and a high-utility frequent sequential patterns discovery module. The new tutoring model of the intelligent tutoring system then offers suggestions to the user in the form of hyperlinks to other knowledge units that are best learned before or after the unit the student has selected. The described system is implemented as a web application called DITUS (Department of Informatics TUtoring System). The validitiy of the proposed model was verified through an experiment wich determined its viability and efficiency

Exploring pedestrian movement patterns

The main objective of this thesis is to develop an approach for exploring, analysing and interpreting movement patterns of pedestrians interacting with the environment. This objective is broken down in sub-objectives related to four research questions. A case study of the movement of visitors in a natural area is used to develop and demonstrate the approach. To achieve the objectives, four research questions were formulated: • How can movement patterns evidencing the stopping behaviour of pedestrians be detected? • What is the validity of the detected movement patterns for describing stopping behaviour of pedestrians? • How can movement patterns be applied to study the movement behaviour of visitors in natural areas? • How can movement patterns be formalized to represent the interactions between pedestrians and between pedestrians and their environment